Stock Market Prediction with Gaussian Naïve Bayes Machine Learning Algorithm

Ernest Kwame Ampomah, Gabriel Nyame, Zhiguang Qin, Prince Clement Addo, Enoch Opanin Gyamfi, Micheal Gyan

Abstract


The stock market is one of the key sectors of a country’s economy. It provides investors with an opportunity to invest and gain returns on their investment. Predicting the stock market is a very challenging task and has attracted serious interest from researchers from many fields such as statistics, artificial intelligence, economics, and finance. An accurate prediction of the stock market reduces investment risk in the market. Different approaches have been used to predict the stock market. The performances of Machine learning (ML) models are typically superior to those of statistical and econometric models. The ability of Gaussian Naïve Bayes ML algorithm to predict stock price movement has not been addressed properly in the existing literature, hence this work attempt to fill that gap by evaluating the performance of GNB algorithm when combined with different feature scaling and feature extraction techniques in stock price movement prediction. The performance of the GNB models set up were ranked using the Kendall’s test of concordance for the various evaluation metrics used. The results indicated that, the predictive model based on integration of GNB algorithm and Linear Discriminant Analysis (GNB_LDA) outperformed all the other models of GNB considered in three of the four evaluation metrics (i.e., accuracy, F1-score, and AUC). Similarly, the predictive model based on integration of GNB algorithm, Min-Max scaling, and PCA produced the best rank using the specificity results. In addition, GNB produced better performance with Min-Max scaling technique than it does with standardization scaling techniques


Full Text:

PDF

References


Fama E. F, Fisher L, Jensen M, Roll R (1969) The adjustment of stock price to new information. Int Eco Rev 10(1):1–21

Yeh, I.-C., & Hsu, T.-K. (2014). Exploring the dynamic model of the returns from value stocks and growth stocks using time series mining. Expert Systems with Applications, 41, 7730–7743.

Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2, 1–8.

Smith, V. L. (2003). Constructivist and ecological rationality in economics. American Economic Review, 93, 465–508.

Huang, C.-J., Yang, D.-X., & Chuang, Y.-T. (2008). Application of wrapper approach and composite classifier to the stock trend prediction. Expert Systems with Applications, 34(4), 2870–2878.

Huang, W., Nakamori, Y., &Wang, S.-Y. (2005). Forecasting stock market movement direction with support vector machine. Computers & Operations Research, 32(10), 2513–2522.

Maragoudakis M., Serpanos D. (2015). Exploiting Financial News and Social Media Opinions for Stock Market Analysis using MCMC Bayesian Inference. Computational Economics. DOI 10.1007/s10614-015-9492-9

Hsu, M.-W., Lessmann, S., Sung, M.-C., Ma, T., & Johnson, J. E. (2016). Bridging the di- vide in financial market forecasting: Machine learners vs. financial economists. Expert Systems with Applications, 61, 215–234.

Weng, B., Ahmed, M. A., & Megahed, F. M. (2017). Stock market one-day ahead movement prediction using disparate data sources. Expert Systems with Applications, 79, 153–163.

Zhang, Y., & Wu, L. (2009). Stock market prediction of s&p 500 via combination of improved bco approach and bp neural network. Expert Systems with Applications, 36 (5), 8849–8854.

Meesad, P., & Rasel, R. I. (2013). Predicting stock market price using support vector regression. In Informatics, electronics & vision (iciev), 2013 international conference on informatics. IEEE, 2013, 1–6.

Jahromi, A. H. Taheri M. A non-parametric mixture of Gaussian naive Bayes classifiers based on local independent features. In 2017 Artificial Intelligence and Signal Processing Conference (AISP). IEEE, 2017, 209-212

Kumar, M., & Thenmozhi,M. (2006). Forecasting Stock index movement: A comparison of support vector machines and random forest. SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, January 24, 2006.

Ou, P., & Wang, H. (2009). Prediction of stock market index movement by ten data mining techniques. Modern Applied Science, 3(12), 28.

Subha, M. V., & Nambi, S. T. (2012). Classification of Stock Index movement using K-nearest neighbours (k-NN) algorithm. WSEAS Transactions on Information Science & Applications, 9(9), 261–270. P259.

De Oliveira, F. A., Nobre, C. N., & Zárate, L. E. (2013). Applying artificial neural networks to prediction of stock price and improvement of the directional prediction index – Case study of PETR4, Petrobras, Brazil. Expert Systems with Applications, 40, 7596–7606.

Zikowski, K. (2015). Using volume weighted support vector machines with walk forward testing and feature selection for the purpose of creating stock trading strategy. Expert Systems with Applications, 42, 1797–1805.

Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015). Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques. Expert Systems with Applications, 42, 259–268.

Sun, S., Wei, Y., & Wang, S. (2018). AdaBoost-LSTM Ensemble Learning for Financial Time Series Forecasting. Computational Science – ICCS 2018, 590–597. doi:10.1007/978-3-319-93713-7_55

Saranya C. Manikandan G. A study on normalization techniques for privacy preserving data mining. International Journal of Engineering and Technology (IJET). 2013, 5(3):2701-2714

Abdi H. Williams L. J. Principal component analysis. Wiley interdisciplinary reviews: computational statistics. 2010, 2(4):433-459

Bro R. Smilde A. K. Principal component analysis. Analytical Methods. 2014;6(9):2812-2831

Tharwat A. Gaber T. Ibrahim A. Hassanien A. E. Linear discriminant analysis: A detailed tutorial. AI communications. 2017, 30(2):169-190

Maskey R. Fei J. Nguyen H. O. Use of exploratory factor analysis in maritime research. The Asian journal of shipping and logistics. 2018, 34(2):91-111




DOI: https://doi.org/10.31449/inf.v45i2.3407

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.